Development of a Continuous Segmented Flow Tubular Reactor and the “Scale‐out” Concept – In Search of Perfect Powders

The synthesis of powders with controlled shape and narrow particle size distributions is still a major challenge for many industries. A continuous Segmented Flow Tubular Reactor (SFTR) has been developed to overcome homogeneity and scale‐up problems encountered when using batch reactors. Supersaturation is created by mixing the co‐reactants in a micromixer inducing precipitation; the suspension is then segmented into identical micro‐volumes by a non‐miscible fluid and sent through a tube. These micro‐volumes are more homogeneous when compared to large batch reactors leading to narrower size distributions, better particle morphology, polymorph selectivity and stoichiometry. All these features have been demonstrated on single tube SFTR for different chemical systems. To increase productivity for commercial application the SFTR is being “scaled‐out” by multiplying the number of tubes running in parallel instead of scaling‐up by increasing their size. The versatility of the multi‐tube unit will allow changes in type of precipitate with a minimum of new investment as new chemistry can be researched, developed and optimised in a single tube SFTR and then transferred to the multi‐tube unit for powder production.     

FuzzyBagging: A novel ensemble of classifiers

In this work, a new method for the creation of classifier ensembles is introduced. The patterns are partitioned into clusters to group together similar patterns, a training set is built using the patterns that belong to a cluster. Each of the new sets is used to train a classifier. We show that the approach here presented, called FuzzyBagging, obtains performance better than Bagging.     

Comparison among feature extraction methods for HIV-1 protease cleavage site prediction

Recently, several works have approached the HIV-1 protease specificity problem by applying a number of methods from the field of machine learning. However, it is still difficult for researchers to choose the best method due to the lack of an effective comparison. For the first time we have made an extensive study on methods for feature extraction for the problem of HIV-1 protease. We show that a fusion of classifiers trained in different feature spaces permits to obtain a drastically error reduction with respect to the performance of the state-of-the-art.     

Compatibilization of an immiscible blend of EPDM and POM with an Ionomer

Immiscible blends of ethylene-propylene-diene-monomer (EPDM) and polyoxymethylene (POM), when EPDM is the major phase were compatibilized on the addition of an ionomer, poly(ethylene-co-methacrylic acid). The inclusion of the ionomer reduced the interfacial tension between the two phases, such that the diameter of the POM domains were significantly reduced to between 0.5 and 2 μm, typical of that required to toughen ductile polymers. The mechanical properties of the resultant compatibilized blends were significantly enhanced with increases in Young's modulus (↑54%), tensile strength (σ, ↑139%), elongation at break (ε, ↑97%), and tensile toughness (↑500%) with increasing ionomer content, relative to EPDM rubber alone. The ShoreA hardness of the compatibilized blend was 70.1 compared with 56.8 for the immiscible binary blend and, 50.2 for neat EPDM rubber.     

High temperature oxidation of CoMn alloys

The oxidation of cobalt-manganese alloys in the range 0–45 wt%Mn corresponding to the stability of the α-phase of cobalt has been studied in the range 750–950°C as an example of binary alloys producing solid solution scales. The alloys oxidize according to a parabolic rate law with a rate constant intermediate between those of the pure metals. The scales were formed mainly by a CoOMnO solid solution with a spinel phase present either as dispersed particles or a continuous layer in the cubic oxide, particularly for the Mn-rich alloys: some degree of internal oxidation has also been observed. The scale properties show a time dependence at constant temperature which is unusual in these systems and is probably related to the presence of the spinel phase. The internal oxidation and the structure and composition of the external scale are discussed with reference to the phase diagram for this system and to the recent theories for the growth of solid solution scales.     

Learning Temperature Dynamics on Agar-Based Phantom Tissue Surface During Single Point CO $$_2$$ 2 Laser Exposure

Neural Processing Letters - This paper presents an approach to determine a model of superficial tissue temperature dynamics during continuous wave CO $$_2$$ laser irradiation. The main contribution...     

Decomposition of Al2TiO5-MgTi2O5 solid solutions: a thermodynamic approach

The decomposition of Al_1–x Mg _x Ti_1+x O₅ solid solutions with x=0.0, 0.1, 0.2, 0.4, 0.5 and 0.6 was studied in the temperature range 900–1175 °C using a 250 h annealing test. As x increases from 0–0.2 there is a strong stabilizing effect and the decomposition temperature decreases from 1280 °C (Al₂TiO₅) down to 1125 °C. For 0.2x0.5 the decomposition temperature does not decrease further. For x=0.6 no decomposition was observed. For x0.5 decomposition is complete or almost complete at 1000 °C; at 900 °C transformation is kinetically hindered and solid solutions with x=0.2 and 0.4 are unaffected by the thermal treatment. A relationship between the decomposition temperature and the parameter x has been derived using the regular solution model to describe the Al_2(1–x)Mg _x Ti_(1+x)O₅ solid solution.     

Anonymization of moving objects databases by clustering and perturbation

Preserving individual privacy when publishing data is a problem that is receiving increasing attention. Thanks to its simplicity the concept of k-anonymity, introduced by Samarati and Sweeney [1], established itself as one fundamental principle for privacy preserving data publishing. According to the k-anonymity principle, each release of data must be such that each individual is indistinguishable from at least k−1 other individuals.     

The role of spectral resolution and classifier complexity in the analysis of hyperspectral images of forest areas

Remote sensing hyperspectral sensors are important and powerful instruments for addressing classification problems in complex forest scenarios, as they allow one a detailed characterization of the spectral behavior of the considered information classes. However, the processing of hyperspectral data is particularly complex both from a theoretical viewpoint [e.g. problems related to the Hughes phenomenon (Hughes, 1968) and from a computational perspective. Despite many previous investigations that have been presented in the literature on feature reduction and feature extraction in hyperspectral data, only a few studies have analyzed the role of spectral resolution on the classification accuracy in different application domains. In this paper, we present an empirical study aimed at understanding the relationship among spectral resolution, classifier complexity, and classification accuracy obtained with hyperspectral sensors for the classification of forest areas. We considered two different test sets characterized by images acquired by an AISA Eagle sensor over 126 bands with a spectral resolution of 4.6 nm, and we subsequently degraded its spectral resolution to 9.2, 13.8, 18.4, 23, 27.6, 32.2 and 36.8 nm. A series of classification experiments were carried out with bands at each of the degraded spectral resolutions, and bands selected with a feature selection algorithm at the highest spectral resolution (4.6 nm). The classification experiments were carried out with three different classifiers: Support Vector Machine, Gaussian Maximum Likelihood with Leave-One-Out-Covariance estimator, and Linear Discriminant Analysis. From the experimental results, important conclusions can be made about the choice of the spectral resolution of hyperspectral sensors as applied to forest areas, also in relation to the complexity of the adopted classification methodology. The outcome of these experiments are also applicable in terms of directing the user towards a more efficient use of the current instruments (e.g. programming of the spectral channels to be acquired) and classification techniques in forest applications, as well as in the design of future hyperspectral sensors.